The Power of Big Data and Learning Analytics

Nicci is the director of Central and Eastern U.S. higher education sales for CDW•G.

Grades, credit hours, rates of participation, work schedules — student information piles up over the years, and colleges and universities continue to uncover new ways to turn all that data into actionable insights.

Admissions departments, in particular, rely heavily on Big Data for recruitment. By purchasing names and predictive modeling services from the ACT, College Board and National Research Center for College and University Admissions, institutions can target those students who are most likely to enroll, saving time and money. Admissions officials can even mine prospective students’ social networks to identify other high-potential candidates.

But Big Data doesn’t just decide which prospects should step onto campus each fall; it can also influence the number of students who walk across the stage four years later. And that sort of power is especially important now, as a national accountability effort forces institutions to identify potential dropouts and raise graduation rates.

According to the Washington Post, Virginia Commonwealth University recently helped to close a gap in student support services by leveraging data to zero in on sophomores and juniors who were at risk of not graduating, despite middle-of-the-road GPAs. The report states that, within a single semester, VCU saw a 16 percent increase in the number of students who successfully completed courses.

University of Tennessee at Chattanooga CIO Tom Hoover told me recently that, as his institution started to use analytics to improve graduation rates, they discovered some interesting things: “We started to examine the graduation rates for our nursing students and found something very interesting. We determined that one of the stumbling blocks that our students were having difficulty with was not a chemistry or biology class, but rather an English class. That English class was actually the class that was forcing them to choose another major when we dug deeper to look at the data.”

Thinking Outside the Box with Higher Ed Big Data

Other colleges and universities buoy student outcomes through even more creative means. Some advise students on their majors by analyzing past course grades to predict future success; others turn data from learning management systems into heat maps that indicate whether students are cramming for class or consistently engaging with coursework.

Ball State University in Indiana uses Big Data to keep an eye on student involvement in campus life activities, which studies indicate are an important success factor. The institution tracks student attendance to campus-sponsored parties through ID cards. If a student’s participation drops, a retention specialist may step in to identify obstacles and offer support.

Further optimizing the college experience, learning analytics and Big Data allow professors to better support struggling students by personalizing the learning process and adapting their teaching when necessary. Institutions can then set and maintain data-driven performance metrics to hold professors accountable for student achievements and failures.

Looking even further down the line, institutions can get a jump on fundraising efforts by predicting the earnings of current students and targeting future boosters. They can also analyze job placement and loan repayment data from government and third-party resources to measure student outcomes long after graduation.

While many colleges and universities already recognize the endless possibilities of Big Data, they should also be aware of a few inherent concerns: Security and data management needs, costs, and legal implications all grow alongside data sets. It’s important for institutions to work with partners who understand the landscape and can guide decision-makers toward a Big Data strategy that works, both now and in the future.

This article is part of EdTech: Focus on Higher Education’s UniversITy blog series.